# Identifying variations in gamma-secretase function that are critical determinants of clinical and biomarker progression of Autosomal Dominant Alzheimer's disease: From mechanism to clinical trials

> **NIH NIH K01** · MASSACHUSETTS GENERAL HOSPITAL · 2024 · $129,166

## Abstract

PROJECT ABSTRACT
There is an urgent need for novel therapies for Alzheimer’s disease (AD) and related dementias (ADRD). The
over-production of longer, aggregation-prone β-amyloid (Aβ) fragments (including Aβ42 and 43) relative to
shorter, non-aggregating fragments (including Aβ37 and 38) are likely critical drivers of disease in both late-
onset, sporadic AD (LOAD) and autosomal dominant AD (ADAD). The balance between production of
aggregating and non-aggregating forms of Aβ is a direct result of the kinetics by which the γ-secretase
complex sequentially cleaves amyloid precursor protein. Alterations in γ-secretase function can have profound
neurodegenerative and cognitive consequences, while modulation of γ-secretase has therapeutic potential in
LOAD and ADAD. Pathogenic variants in Presenilin-1 (PSEN1), the key catalytic subunit in the γ-secretase
complex, are the most common cause of ADAD. There is substantial heterogeneity in Age of Symptom Onset
(a range of >30 years) and biomarker progression across PSEN1 variants. The proposed K01 project will
characterize differences in γ-secretase function across ~250 unique ADAD-causing PSEN1 variants in the
presence and absence of two structurally distinct γ-secretase modulators (GSMs), measured in a cell-based
model system (Aim 1). In Aim 2, variant-specific characteristics will be combined with cross-sectional cognitive
and biomarker measures from carriers of corresponding PSEN1 variants participating in the Dominantly
Inherited Alzheimer’s Network Observational Study (DIAN-Obs), a large international study in which over 80
unique PSEN1 pathogenic variants are represented. This translational dataset will be used to examine whether
differences in γ-secretase function between PSEN1 mutations can account for the heterogeneity in Age of
Symptom Onset, cognition, and AD biomarkers. Aim 3 will utilize longitudinal data from DIAN-Obs to examine
whether mutation-specific characteristics 1) predict change in biomarkers and cognition and 2) increase power
to detect estimated treatment effects for relevant trial outcomes. Leveraging available data from DIAN Trials
Unit, exploratory analyses will assess whether inclusion of variant-dependent characteristics can improve
stratification approaches for future clinical trials. This K01 will elucidate mechanisms underlying AD, facilitate
ongoing ADAD clinical trials, and support the development of GSMs as possible therapeutics. To help Dr.
Stephanie Schultz achieve these aims, a multidisciplinary mentorship team has been assembled from the
Harvard Medical School community and DIAN Leadership team to complement coursework in protein
characterization, translational research, advanced statistical analysis, and clinical trials. Dr. Jasmeer Chhatwal
will be the primary mentor overseeing research and career progress. Dr. Schultz will receive mentoring from
Dr. Dennis Selkoe on the mechanisms underlying AD, Drs. Reisa Sperling and Eric McDade on translational
and clinical tr...

## Key facts

- **NIH application ID:** 10783323
- **Project number:** 1K01AG084816-01
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Stephanie Schultz
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $129,166
- **Award type:** 1
- **Project period:** 2023-12-01 → 2028-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10783323

## Citation

> US National Institutes of Health, RePORTER application 10783323, Identifying variations in gamma-secretase function that are critical determinants of clinical and biomarker progression of Autosomal Dominant Alzheimer's disease: From mechanism to clinical trials (1K01AG084816-01). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10783323. Licensed CC0.

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